Considerable diversity gains and multiplexing gains can be obtained by using a Multi Input Multi Output (MIMO) technology, so that reliability and a transmission rate of a wireless communications system are improved. Currently, the technology has been widely applied to various wireless communications systems, such as a Long Term Evolution (LTE) system and a Worldwide Interoperability for Microwave Access (WiMax) system. However, with increase in a quantity of antennas at a transmit end and a receive end, a quantity of unknown channels also increases. For example, the 3rd Generation Partnership Project (3GPP) release 11 supports a downlink 8×8 MIMO architecture; before performing signal demodulation, the receive end needs to acquire channel state information (CSI) of 8×8=64 channels; if the transmit end further needs to perform MIMO beamforming, the receive end further needs to feed back the CSI to the transmit end. Therefore, accuracy of the CSI directly determines performance of an MIMO system. FIG. 1 is a schematic diagram of multipath transmission in an MIMO system.
To improve accuracy of CSI, a quantity of pilots is generally increased, which may reduce spectrum utilization and limit performance improvement of the MIMO system instead. For least square (LS) channel estimation, if a quantity of pilots needs to be greater than a maximum delay spread sampling value of a channel, relatively large pilot overheads are generally required. In view of this problem, sparse channel estimation is proposed, and a channel impulse response (CIR) is reconstructed by using an idea of sparsity recovery, which can fully exploit sparsity of a wireless channel and an advantage of a compressed sensing (CS) signal processing technology. Compared with the LS channel estimation, the sparse channel estimation can significantly reduce pilot overheads, and improve spectrum utilization.
To improve performance of the sparse channel estimation, an optimal pilot arrangement of a sparse channel needs to be determined. Currently, there are the following two common solutions for determining a pilot arrangement:
The prior art 1 is a pilot arrangement manner of equal space distribution, and this manner is generally used in an LTE system, where pilot arrangements are evenly distributed in a frequency domain and in a time domain. However, many research literatures indicate that a pilot arrangement determined for a sparse channel according to this manner is not optimized.
The prior art 2 is a random pilot arrangement manner. For example, for an MIMO system with two transmit antennas and 256 available orthogonal frequency division multiplexing (OFDM) subcarriers, if each transmit antenna uses 12 subcarriers as pilot subcarriers, 24 subcarriers are randomly selected from the 256 available OFDM subcarriers. Then 12 subcarriers are randomly selected from the selected 24 subcarriers and are allocated to a transmit antenna 1, and remaining 12 subcarriers are allocated to a transmit antenna 2. In this way, pilot subcarriers of different transmit antennas are orthogonal to each other, the two transmit antennas may send data simultaneously, and signals transmitted from the two different antennas can be effectively distinguished when sparse channel estimation is performed at a receive end. However, this manner still cannot ensure that a determined pilot arrangement is an optimized pilot arrangement.